Building the Next Generation of Context-Aware AI Agents
From text generation to real-world action
graph TB
A["🧠 Traditional LLM<br/>Text Generator"]
B["🤖 Agentic AI<br/>Action Taker"]
A --> C["📝 Email Drafts"]
A --> D["💭 Ideas"]
B --> E["🔍 Research"]
B --> F["⚙️ Tool Use"]
B --> G["🎯 Goal Achievement"]
style A fill:#f9f,stroke:#333,stroke-width:2px
style B fill:#9f9,stroke:#333,stroke-width:2px
The paradigm shift: Passive content creation → Active problem-solving
LLMs are “incredibly smart in a vacuum” 🎈
| Aspect | Traditional (N×M) | MCP Standard |
|---|---|---|
| Integration Effort | High: Custom code for each pair | Low: One server, any client |
| Discoverability | Manual documentation reading | Automated runtime reflection |
| Interoperability | Vendor-locked | Model-agnostic open standard |
| Security | Ad-hoc API key management | OAuth 2.1, scopes, consent |
| Scalability | Poor: N×M complexity | Excellent: N+M complexity |
Model Context Protocol - Anthropic, November 2024
graph TD
A[MCP Host<br/>Claude Desktop, Cursor IDE] --> B[MCP Client 1]
A --> C[MCP Client 2]
A --> D[MCP Client N]
B --> E[ArXiv Server]
C --> F[GitHub Server]
D --> G[Database Server]
style A fill:#e1f5fe
style E fill:#f3e5f5
style F fill:#f3e5f5
style G fill:#f3e5f5
Goal: Build an agent that finds, analyzes, and manages academic papers
Run and test: uv run uvicorn server:mcp --reload
| Primitive | Purpose | Analogy | Example |
|---|---|---|---|
| Tools | Executable actions with side effects | POST Request | download_paper(paper_id) |
| Resources | Read-only, file-like data | GET Request | arxiv/{paper_id}/abstract |
| Prompts | Reusable workflow templates | Workflow | “Deep Paper Analysis” |
Benefits: Clear separation of concerns, better security, predictable behavior
Setup & Models
Tools & Resources
@mcp.tool()
def search_papers(query: str, max_results: int = 5):
"""Search ArXiv for papers"""
search = arxiv.Search(query=query,
max_results=max_results)
return [Paper(...) for r in search.results()]
@mcp.resource("arxiv/{paper_id}/abstract")
def get_abstract(paper_id: str) -> str:
"""Get paper abstract"""
# Implementation here
passFull implementation: See examples/arxiv-server/server.py
Challenge: Access private Zotero library safely
Result: Most secure path is also the easiest path
sequenceDiagram
participant User
participant Client
participant Server
participant Zotero
User->>Client: "Save paper to Zotero"
Client->>Server: add_paper_to_collection()
Server-->>Client: 401 Unauthorized
Client->>Zotero: OAuth authorization request
Zotero->>User: Login & consent screen
User->>Zotero: Approve access
Zotero->>Client: Authorization code
Client->>Zotero: Exchange for access token
Client->>Server: Retry with Bearer token
Server->>Client: Success!
Ask users for clarification when needed
Example: “Which ‘transformers’?” - AI models or electrical engineering?
Key benefit: Human-in-the-loop workflows with graceful ambiguity handling
200+ servers and growing - Build once, use everywhere
Let’s see our AI Research Assistant in action
MCP resolves the critical context gap
The time to start building is now.
Thank you!
Let’s build the future of AI together.
MCP 101 - Model Context Protocol